ArticleJournal of advanced research2025
DeepB
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- GANs in Peptide Drug Discovery: From De Novo Design to Multi-Property Optimization and Clinical Translation.Probiotics and antimicrobial proteins · 2026Review
- Identification of Novel AChE-Targeting Neuroprotective Peptides from Pacific Oyster (Marine drugs · 2026Article
- WaveUNet+: Preserving Root System Architecture Integrity in In Situ Root Segmentation via a Unified Spectral-Spatial Framework.Plants (Basel, Switzerland) · 2026Article
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- cncFinder: A graph-attention-network-based interpretable learning model to identify bifunctional long non-coding RNAs.Molecular therapy. Nucleic acids · 2026Article
- Peptide-Based Therapeutics for Alzheimer's Disease: Medicinal Chemistry, AI-Guided Computational Design, and Blood-Brain Barrier Delivery.Drug design, development and therapy · 2026Review
- Prediction of blood-brain barrier-penetrating peptides using B3BPFN.Frontiers in molecular biosciences · 2026Article
- Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines.Innovation (Cambridge (Mass.)) · 2025Article
- DSF-YOLO for robust multiscale traffic sign detection under adverse weather conditions.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe blood-brain barrier (BBB) serves as a critical structural barrier and impedes the entry of most neurotherapeutic drugs into the brain. This poses substantial challenges for central nervous system (CNS) drug development, as there is a lack of efficient drug delivery technologies to overcome this obstacle. BBB penetrating peptides (BBBPs) hold promise in overcoming the BBB and facilitating the delivery of drug molecules to the brain. Therefore, precise identification of BBBPs has become a crucial step in CNS drug development. However, most computational methods are designed based on conventional models that inadequately capture the intricate interaction between BBBPs and the BBB. Moreover, the performance of these methods was further hampered by unbalanced datasets.
objectivesThis study addresses the problem of unbalanced datasets in BBBP prediction and proposes a powerful predictor for efficiently and accurately identifying BBBPs, as well as generating analogous BBBPs.
methodsA transformer-based deep learning model, DeepB
resultsThe FBGAN model possesses the ability to generate novel BBBP-like peptides, effectively mitigating the data imbalance in BBBP prediction. Extensive experiments on benchmarking datasets demonstrated that DeepB
conclusionThe interpretable analyses provided by DeepB
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.